Triple
T36837310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo le Gris |
E910307
|
entity |
| Predicate | hasGivenNameOfRealPerson |
P186567
|
FINISHED |
| Object | León |
—
|
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: León | Statement: [Leo le Gris, hasGivenNameOfRealPerson, León]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGivenNameOfRealPerson Context triple: [Leo le Gris, hasGivenNameOfRealPerson, León]
-
A.
hasNameGivenTo
Indicates that one entity is the name that has been assigned or given to another entity.
-
B.
isGivenNameOfFictionalCharacter
Indicates that a given name is the personal name borne by a fictional character.
-
C.
notRealNameOf
Indicates that the referenced name is not the entity’s actual or official name (e.g., it is false, fabricated, or otherwise not the real name of that entity).
-
D.
hasNamedAfterPerson
Indicates that one entity is named in honor of, or derived from the name of, a specific person.
-
E.
namedAfterFictionalCharacter
Indicates that one entity has been given its name in honor of, or derived from, a fictional character.
- F. None of above. chosen
Provenance (4 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_69f76e7e9d60819092442fba73290a46 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.