Triple
T11901319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Chance City |
E283156
|
entity |
| Predicate | hasWord |
P35
|
FINISHED |
| Object | Chance |
E300778
|
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: Chance | Statement: [Second Chance City, hasWord, Chance]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chance Context triple: [Second Chance City, hasWord, Chance]
-
A.
Chance
chosen
Chance is a masculine given name often associated with notions of luck, opportunity, and fortune.
-
B.
Luck
Luck is a common English surname borne by various notable individuals in sports, entertainment, and other fields.
-
C.
Luck
"Luck" is a 2022 animated fantasy comedy film about a perpetually unlucky girl who discovers a secret world of good and bad luck.
-
D.
Luck
Luck is an American television drama series centered on the world of horse racing and gambling, known for its ensemble cast and gritty portrayal of the racing industry.
-
E.
Luck By Chance
Luck By Chance is a 2009 Hindi-language satirical drama film about the struggles and compromises of aspiring actors in the Bollywood film industry.
- 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_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8dd16433881909befca9774bdaab4 |
completed | April 10, 2026, 11:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4182f18c08190b22706b024d60dd7 |
completed | May 1, 2026, 3:04 a.m. |
Created at: April 8, 2026, 9:44 p.m.