Triple
T8491369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maiden Bradley |
E200976
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Mere |
E547023
|
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: Mere | Statement: [Maiden Bradley, locatedNear, Mere]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mere Context triple: [Maiden Bradley, locatedNear, Mere]
-
A.
Mere
chosen
Mere is a village and civil parish in Cheshire, England, known for its affluent residential character and proximity to the town of Knutsford.
-
B.
Mera
Mera is a powerful Atlantean warrior and sorceress from DC Comics, best known as Aquaman’s ally and queen of Atlantis.
-
C.
Ménaka
Ménaka is a town in eastern Mali that serves as an important administrative and trading center in the Sahel region.
-
D.
Mele
Mele is a coastal village on the island of Efate in Vanuatu, known for its traditional Ni-Vanuatu culture and proximity to popular natural attractions.
-
E.
Marella
Marella is an Italian feminine given name, notably borne by Marella Agnelli, a prominent socialite, art collector, and style icon.
- 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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe55af3f48190a8cd64cdce0ebd4c |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a54c3888190b11b7e9909abe518 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:13 p.m.