Triple
T4237504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inés Zorreguieta |
E94727
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Inés |
E398718
|
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: Inés | Statement: [Inés Zorreguieta, givenName, Inés]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inés Context triple: [Inés Zorreguieta, givenName, Inés]
-
A.
Inés
chosen
Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
-
B.
Pilar
Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
-
C.
Pilar
Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
-
D.
Pilar
Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
-
E.
Pilar
Pilar is a coastal municipality in the Philippine province of Bataan known for its historical significance in World War II and its role in the defense of Bataan.
- 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_69b34537cc6481909cd0a96acbb33ef7 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e7589b48190a16e7ff29fb6a162 |
completed | March 12, 2026, 11:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a86996f48190987d3ac234a9b7f4 |
completed | March 14, 2026, 6:26 p.m. |
Created at: March 12, 2026, 11:05 p.m.