Triple
T3525495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peggy Lipton |
E74528
|
entity |
| Predicate | role |
P268
|
FINISHED |
| Object |
Julie Barnes
Julie Barnes is a fictional undercover police officer and one of the main characters in the late-1960s/early-1970s American TV series "The Mod Squad."
|
E382145
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Julie Barnes | Statement: [Peggy Lipton, role, Julie Barnes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Barnes Context triple: [Peggy Lipton, role, Julie Barnes]
-
A.
Sarah Barnard
Sarah Barnard was the wife of renowned English scientist Michael Faraday, providing personal support throughout his career in 19th-century London.
-
B.
Jill Bilcock
Jill Bilcock is an acclaimed Australian film editor known for her work on major films such as "Moulin Rouge!", "Romeo + Juliet," and "Elizabeth."
-
C.
Julie Buck
Julie Buck is a member of the Buck family, known primarily as a relative of American sportscaster Joe Buck.
-
D.
Liz Hannah
Liz Hannah is an American screenwriter and producer best known for co-writing the acclaimed historical drama film "The Post."
-
E.
Laura Mennell
Laura Mennell is a Canadian actress known for her roles in science fiction and fantasy film and television, including appearances in projects like Watchmen and the series Alphas.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Julie Barnes Triple: [Peggy Lipton, role, Julie Barnes]
Generated description
Julie Barnes is a fictional undercover police officer and one of the main characters in the late-1960s/early-1970s American TV series "The Mod Squad."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Julie Barnes Target entity description: Julie Barnes is a fictional undercover police officer and one of the main characters in the late-1960s/early-1970s American TV series "The Mod Squad."
-
A.
Sarah Barnard
Sarah Barnard was the wife of renowned English scientist Michael Faraday, providing personal support throughout his career in 19th-century London.
-
B.
Jill Bilcock
Jill Bilcock is an acclaimed Australian film editor known for her work on major films such as "Moulin Rouge!", "Romeo + Juliet," and "Elizabeth."
-
C.
Julie Buck
Julie Buck is a member of the Buck family, known primarily as a relative of American sportscaster Joe Buck.
-
D.
Liz Hannah
Liz Hannah is an American screenwriter and producer best known for co-writing the acclaimed historical drama film "The Post."
-
E.
Laura Mennell
Laura Mennell is a Canadian actress known for her roles in science fiction and fantasy film and television, including appearances in projects like Watchmen and the series Alphas.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85d0c5488190a3d8e02ebd01a1aa |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc6a8d0c819094d38b9c47fb67b4 |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cdd2715c81908250bb2925de8e1f |
completed | March 14, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69b4ce71b9e4819089d4b74cad82fa23 |
completed | March 14, 2026, 2:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4d21472648190a1ef55af8c046182 |
completed | March 14, 2026, 3:12 a.m. |
Created at: March 8, 2026, 3:19 p.m.