Triple
T6092511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Field of Blood |
E135800
|
entity |
| Predicate | hasCauseOfName |
P7885
|
FINISHED |
| Object | blood money paid to Judas |
—
|
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: blood money paid to Judas | Statement: [Field of Blood, hasCauseOfName, blood money paid to Judas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCauseOfName Context triple: [Field of Blood, hasCauseOfName, blood money paid to Judas]
-
A.
usesNameDueTo
Indicates that one entity adopts or applies a particular name for another entity specifically because of some motivating reason, circumstance, or dependency.
-
B.
reasonForName
chosen
Indicates the explanation or cause behind why an entity has a particular name.
-
C.
hasNameOrigin
Indicates that the origin or source of an entity’s name is specified by the related entity.
-
D.
hasGivenNameBasis
Indicates that one entity’s given name is derived from, based on, or formed using another entity (such as a name, word, or person) as its basis.
-
E.
hasEponymConnectionTo
Indicates that one entity is named after, derived from, or otherwise linguistically or honorifically connected to another entity as its eponym.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ac8c7481909fb22cf157be45ce |
completed | March 22, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.