Triple
T12746023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roland Petit |
E304605
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Petit |
E671165
|
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: Petit | Statement: [Roland Petit, familyName, Petit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Petit Context triple: [Roland Petit, familyName, Petit]
-
A.
Petit
chosen
Petit is the surname of Sir Dinshaw Maneckji Petit, 1st Baronet, a prominent 19th-century Parsi industrialist and philanthropist from Bombay.
-
B.
Petit Pic
Petit Pic is the lower of the two main summits of the Pic du Midi d’Ossau in the French Pyrenees, known for its rugged alpine climbing routes.
-
C.
Kleiner
Kleiner is a surname most notably associated with Eugene Kleiner, a pioneering Silicon Valley venture capitalist and co-founder of the firm Kleiner Perkins.
-
D.
Petit Bé fort
Petit Bé fort is a tidal island fortress off Saint-Malo, France, built in the late 17th century as part of the town’s coastal defenses.
-
E.
Little
Little is a 2019 fantasy-comedy film in which a domineering tech executive is magically transformed into her younger self, forcing her to relive middle school and confront her past behavior.
- 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96bd42fe08190a85467b1a998d2af |
completed | April 10, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c94265481908ace9cac757df890 |
completed | May 2, 2026, 10:37 p.m. |
Created at: April 9, 2026, 5:27 p.m.