Triple
T6604632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Treiman |
E149082
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sam |
E64126
|
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: Sam | Statement: [Sam Treiman, givenName, Sam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Context triple: [Sam Treiman, givenName, Sam]
-
A.
Sam
chosen
Sam is a person whose given name is Sam.
-
B.
Simon
Simon is a common masculine given name of Hebrew origin, widely used in many cultures and languages.
-
C.
Simon
Simon is the central character in Ang Lee's 1993 film "The Wedding Banquet," a Taiwanese American man who enters a sham marriage to appease his traditional parents while secretly living with his male partner in New York.
-
D.
Simon
Simon is a common surname of English and Jewish origin borne by numerous notable individuals across politics, business, arts, and sciences.
-
E.
Simon
Simon is a sleazy used-car salesman and comic-relief character in the action-comedy film "True Lies," who pretends to be a secret agent to seduce women.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af13151c81909b68fa6c77e1c482 |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbc8f1308190a4afcf10a5a7105e |
completed | March 27, 2026, 6:26 p.m. |
Created at: March 27, 2026, 1:56 p.m.