Triple
T26528703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarín |
E670759
|
entity |
| Predicate | hasFamousLineAbout |
P160615
|
FINISHED |
| Object | desire to avoid taking sides |
—
|
LITERAL 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: desire to avoid taking sides | Statement: [Clarín, hasFamousLineAbout, desire to avoid taking sides]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamousLineAbout Context triple: [Clarín, hasFamousLineAbout, desire to avoid taking sides]
-
A.
famousLineSpeaker
Indicates that the subject is the person who spoke or delivered the famous line referenced by the object.
-
B.
isFamousExcerptOf
Indicates that one text passage is a well-known or widely recognized excerpt taken from a larger work.
-
C.
hasIconicDialogue
chosen
Indicates that an entity (such as a work or scene) contains dialogue that is widely recognized, memorable, and strongly associated with it.
-
D.
characterCatchphrase
Indicates that a particular phrase is commonly and distinctively used by a character as their catchphrase.
-
E.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
- 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_69eeb31ea1e08190b9ff43cf9bc25bf8 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f68805b4848190b75da14996d52a38 |
completed | May 2, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 27, 2026, 1:34 a.m.