Triple
T27792465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACC |
E701117
|
entity |
| Predicate | hasLanguageNeutralForm |
P187568
|
FINISHED |
| Object | ACC |
unclear NED1
|
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: ACC | Statement: [ACC, hasLanguageNeutralForm, ACC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageNeutralForm Context triple: [ACC, hasLanguageNeutralForm, ACC]
-
A.
hasLanguageFormOf
Indicates that one entity is a specific linguistic form, expression, or realization of the language used by another entity.
-
B.
hasNeuterForm
Indicates that an entity has a grammatical form specifically used for neuter gender.
-
C.
hasFeminineFormInSomeLanguages
Indicates that the referenced entity has a distinct feminine grammatical or lexical form in at least one language.
-
D.
hasMorphologicalForm
Indicates that one entity is a specific morphological form or inflected variant of another entity.
-
E.
hasLanguageRepresentation
Indicates that an entity is expressed, encoded, or represented using a particular natural or formal language.
- F. None of above. chosen
Provenance (5 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_69ef6a50d8088190acbf3dfbb06d8091 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12ecb9030881909e78ab07fc8505a3 |
completed | May 24, 2026, 12:19 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
| PDg | Predicate description generation | batch_69fb6fdab95c81909acff3c6a2359787 |
completed | May 6, 2026, 4:44 p.m. |
Created at: April 27, 2026, 5:29 p.m.