Triple
T6495670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Once Again |
E148152
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Maxine |
E25009
|
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: Maxine | Statement: [Once Again, hasCharacter, Maxine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maxine Context triple: [Once Again, hasCharacter, Maxine]
-
A.
Maxine
chosen
Maxine is a character featured in the film "Once Again."
-
B.
Maxine Cooper
Maxine Cooper was an American actress best known for her role as Velda, the loyal secretary in the classic 1955 film noir "Kiss Me Deadly."
-
C.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06ab958808190bd85e007e925ffc4 |
completed | March 22, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c65fdf59548190b4ba4f44716e6b3c |
completed | March 27, 2026, 10:45 a.m. |
Created at: March 22, 2026, 4:53 p.m.