Triple
T5779071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Jackman |
E127512
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Logan (2017 film) |
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 (2017 film) | Statement: [Hugh Jackman, notableWork, Logan (2017 film)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Logan (2017 film) Context triple: [Hugh Jackman, notableWork, Logan (2017 film)]
-
A.
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.
-
B.
Logan
Logan is a neighborhood in Wyoming, Ohio, that serves as the community surrounding the Wyoming train station.
-
C.
Logan
Logan is a city in northern Utah known for being home to Utah State University and for its scenic Cache Valley setting.
-
D.
Logan
Logan is a small city in southern West Virginia that serves as a local hub for the surrounding coal-mining region.
-
E.
Logan
Logan is a name commonly used as both a given name and surname in English-speaking countries.
- 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_69c008361fa88190aefa4dc41b051e7f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029e26ff88190b7f8eb03bcd30dc6 |
completed | March 22, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b0d1ddb48190a697f202b43cc633 |
completed | March 23, 2026, 3:17 a.m. |
Created at: March 22, 2026, 3:50 p.m.