Triple
T25610696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foucault’s Collège de France lecture publications |
E642040
|
entity |
| Predicate | cityOfLectures |
P71205
|
FINISHED |
| Object | Paris |
—
|
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: Paris | Statement: [Foucault’s Collège de France lecture publications, cityOfLectures, Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityOfLectures Context triple: [Foucault’s Collège de France lecture publications, cityOfLectures, Paris]
-
A.
lecturesHeldIn
Indicates that a lecture event takes place or is conducted within a specific location or venue.
-
B.
taughtInCity
chosen
Indicates that a teaching activity or educational instruction took place within a specific city.
-
C.
gaveLecturesAt
Indicates that a person delivered lectures or taught courses at a particular institution or location.
-
D.
yearLecturesDelivered
Indicates the specific year in which the lectures were delivered.
-
E.
cityCourse
Indicates a relationship where a course, route, or path runs through, connects, or is located within a particular city.
- 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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f9e2a5e08190bb4740fc7b758a49 |
completed | May 2, 2026, 1:19 p.m. |
| PD | Predicate disambiguation | batch_69f480789be08190ab252a6de3797200 |
completed | May 1, 2026, 10:29 a.m. |
Created at: April 21, 2026, 4:41 p.m.