Triple
T9969889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Life in Pieces |
E196176
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Lark Short
Lark Short is a recurring child character on the American sitcom "Life in Pieces," appearing as a member of the Short family.
|
E831776
|
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: Lark Short | Statement: [Life in Pieces, featuresCharacter, Lark Short]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lark Short Context triple: [Life in Pieces, featuresCharacter, Lark Short]
-
A.
Lark
Lark was a famous overnight passenger train that ran between San Francisco and Los Angeles, known for its streamlined design and sleeper service.
-
B.
Lark
Lark is a brand of cigarettes historically marketed by Liggett & Myers and known for its distinctive charcoal filter.
-
C.
Lark
Lark is a cloud-based workplace collaboration and productivity platform offering integrated messaging, video conferencing, calendars, and document tools, developed under ByteDance.
-
D.
Larke Recchie
Larke Recchie is an American attorney and advocate best known as the wife of U.S. Senator Sherrod Brown.
-
E.
Lucetta Creeson
Lucetta Creeson is a fictional character appearing in the narrative of the work titled "Stone."
- 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: Lark Short Triple: [Life in Pieces, featuresCharacter, Lark Short]
Generated description
Lark Short is a recurring child character on the American sitcom "Life in Pieces," appearing as a member of the Short family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lark Short Target entity description: Lark Short is a recurring child character on the American sitcom "Life in Pieces," appearing as a member of the Short family.
-
A.
Lark
Lark was a famous overnight passenger train that ran between San Francisco and Los Angeles, known for its streamlined design and sleeper service.
-
B.
Lark
Lark is a brand of cigarettes historically marketed by Liggett & Myers and known for its distinctive charcoal filter.
-
C.
Lark
Lark is a cloud-based workplace collaboration and productivity platform offering integrated messaging, video conferencing, calendars, and document tools, developed under ByteDance.
-
D.
Larke Recchie
Larke Recchie is an American attorney and advocate best known as the wife of U.S. Senator Sherrod Brown.
-
E.
Lucetta Creeson
Lucetta Creeson is a fictional character appearing in the narrative of the work titled "Stone."
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb7b7ea9881908a56f11e2e446dd0 |
completed | April 2, 2026, 12:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23dca14d081909573e91a576921c9 |
completed | April 5, 2026, 10:47 a.m. |
| NEDg | Description generation | batch_69d23f63a0d08190a3ace2e4bb58a9ac |
completed | April 5, 2026, 10:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d23ff6fdc4819082d8d575a3901ea8 |
completed | April 5, 2026, 10:56 a.m. |
Created at: March 30, 2026, 8:48 p.m.