Triple
T3228192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harper Beckham |
E67673
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Harper |
E335446
|
NE FINISHED |
How this triple was built (2 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: Harper | Statement: [Harper Beckham, givenName, Harper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harper Context triple: [Harper Beckham, givenName, Harper]
-
A.
Harper
Harper is a major American publishing house known for releasing a wide range of influential fiction and nonfiction works.
-
B.
Harper Grace Burtka-Harris
chosen
Harper Grace Burtka-Harris is one of the fraternal twin children of actor Neil Patrick Harris and his husband, chef and actor David Burtka.
-
C.
Spencer
Spencer is a small town in central Massachusetts known for its New England character and historic mill village roots.
-
D.
Spencer
Spencer is the middle name of American author and aviator Anne Spencer Lindbergh, reflecting her family’s naming tradition.
-
E.
Spencer
Spencer is a masculine given name of English origin, historically associated with roles such as steward or dispenser and borne by various notable figures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaeb6f8588190a33a9d6c779e8992 |
completed | March 8, 2026, 5:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b262675b588190bcff98e7fa3a0c77 |
completed | March 12, 2026, 6:51 a.m. |
Created at: March 8, 2026, 3:08 p.m.