Triple
T8540040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Mangold |
E202171
|
entity |
| Predicate | screenwriterOf |
P2831
|
FINISHED |
| Object | Logan |
E187033
|
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: Logan | Statement: [James Mangold, screenwriterOf, Logan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Logan Context triple: [James Mangold, screenwriterOf, Logan]
-
A.
Logan
Logan is a name commonly used as both a given name and surname in English-speaking countries.
-
B.
Logan
Logan is a small village in eastern New Mexico, United States, known for its proximity to Ute Lake and its role as a local recreational and service hub in Quay County.
-
C.
Logan
Logan is a small city in southern West Virginia that serves as a local hub for the surrounding coal-mining region.
-
D.
Logan
chosen
Logan is a 2017 superhero film in the X-Men franchise that follows an aging Wolverine on a violent, character-driven road journey in a bleak near-future.
-
E.
Logan
Logan is a neighborhood in Wyoming, Ohio, that serves as the community surrounding the Wyoming train station.
- 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6dfb2bc8190a41e32eca3c824c2 |
completed | March 31, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea86036a881909cd1744cdb5b7a7f |
completed | April 2, 2026, 5:33 p.m. |
Created at: March 30, 2026, 6:18 p.m.