Triple
T11239698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carolyn Perron |
E266037
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Perron |
E300750
|
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: Perron | Statement: [Carolyn Perron, familyName, Perron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Perron Context triple: [Carolyn Perron, familyName, Perron]
-
A.
Perron
chosen
Perron is a surname of French origin, often considered a variant of the name Perrin.
-
B.
Rattenberg
Rattenberg is a small municipality in the Straubing-Bogen district of Lower Bavaria, Germany, known for its rural setting and traditional Bavarian character.
-
C.
Pero
Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
-
D.
Pero
Pero is a West Chadic language spoken in parts of Nigeria.
-
E.
Tous
Tous is a Spanish jewelry and accessories brand known for its distinctive teddy bear logo and affordable luxury designs.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e918375081908c2a7ccb50cbf331 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad6e9390819085d10635cb039f85 |
completed | April 19, 2026, 10:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.