Triple
T12645834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleanor Parker |
E302020
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Parker |
E44427
|
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: Parker | Statement: [Eleanor Parker, familyName, Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parker Context triple: [Eleanor Parker, familyName, Parker]
-
A.
Parker
Parker is a 2013 American crime thriller film starring Jason Statham as a professional thief who seeks revenge after being double-crossed by his crew.
-
B.
Parker
chosen
Parker is a common English surname borne by numerous notable individuals across fields such as politics, sports, arts, and science.
-
C.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
D.
Tucker
Tucker is a paranormal investigator character from the Insidious horror film series, known for his tech-based ghost-hunting work alongside his partner Specs.
-
E.
Tucker
Tucker is a masculine given name most prominently associated with American conservative political commentator Tucker Carlson.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9614cefdc81908cfc4a4d04aa6eda |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6687961d88190a605f17425ad7547 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:17 p.m.