Triple
T9947106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abra Stone |
E195230
|
entity |
| Predicate | hasAlias |
P455
|
FINISHED |
| Object | Shiner |
E501587
|
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: Shiner | Statement: [Abra Stone, hasAlias, Shiner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiner Context triple: [Abra Stone, hasAlias, Shiner]
-
A.
Shiner
chosen
Shiner is a film scored by Australian composer Paul Grabowsky, noted for its atmospheric and jazz-influenced soundtrack.
-
B.
Seltz
Seltz is the principal city and administrative center of the Imperial City of Seltz.
-
C.
Sweetwater
Sweetwater is a small West Texas city best known for its historic role in the oil and cattle industries and for hosting one of the world’s largest rattlesnake roundups.
-
D.
Sweetwater
Sweetwater is a 2013 American Western thriller film starring January Jones as a frontier woman seeking violent revenge in 19th-century New Mexico.
-
E.
Southern Comfort
Southern Comfort is a 1981 action-thriller film about a group of National Guard soldiers lost in the Louisiana bayou who are hunted by local Cajuns after a violent misunderstanding.
- 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_69ca82e96a108190932bd1fc4acd73a0 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb657b35c81909448e93999f6e77c |
completed | April 2, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2292257bc8190b3a15de60d7c9ba2 |
completed | April 5, 2026, 9:19 a.m. |
Created at: March 30, 2026, 8:45 p.m.