Triple
T11380056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Bowden |
E269570
|
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 Bowden, givenName, Sam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Context triple: [Sam Bowden, givenName, Sam]
-
A.
Sam
Sam is the iconic, burlap-sack-masked embodiment of Halloween who enforces the holiday’s rules in the Trick 'r Treat horror franchise.
-
B.
Sam
chosen
Sam is a person whose given name is Sam.
-
C.
Simon
Simon is a common masculine given name of Hebrew origin, widely used in many cultures and languages.
-
D.
Simon
Simon is a 1980 satirical science-fiction comedy film written and directed by Marshall Brickman, centered on a psychology professor who is tricked into believing he is an alien.
-
E.
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.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc331f188190a7f69f1aae53fb6b |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e556a10f988190b173dc4880a8c6c6 |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:34 p.m.