Triple
T35210044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Farhi |
E1016647
|
entity |
| Predicate | eponymousLabel |
P116025
|
FINISHED |
| Object | Nicole Farhi |
—
|
NE NERFINISHED |
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: Nicole Farhi | Statement: [Nicole Farhi, eponymousLabel, Nicole Farhi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eponymousLabel Context triple: [Nicole Farhi, eponymousLabel, Nicole Farhi]
-
A.
eponymFor
Indicates that one entity gives its name to another entity, which is then named after it.
-
B.
hasEponymousLabel
chosen
Indicates that something is named after a particular person, place, or entity, serving as its eponym.
-
C.
eponymLatinName
Indicates that one entity is the Latin name that is an eponym derived from or named after another entity.
-
D.
eponymOriginCountry
Indicates the country from which the person or entity that gave its name (as an eponym) to something originates.
-
E.
eponymProfession
Indicates that a person’s profession is the source of an eponym, i.e., a word or name derived from that professional role.
- F. None of above.
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_69f76ddf549c8190869d0af076fd2c28 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78f63c8788190b253a18de5ca1312 |
completed | May 3, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:02 p.m.